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Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms,alongside supervised learning and unsupervised learning.
Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data,known as training data,in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications,such as in medicine,email filtering,speech recognition,and computer vision,where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Stochastic gradient descent is an iterative method for optimizing an objective function with suitable smoothness properties. It can be regarded as a stochastic approximation of gradient descent optimization,since it replaces the actual gradient by an estimate thereof. Especially in high-dimensional optimization problems this reduces the computational burden,achieving faster iterations in trade for a lower convergence rate.
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Many classes of convex optimization problems admit polynomial-time algorithms,whereas mathematical optimization is in general NP-hard.
In probability theory and machine learning,the multi-armed bandit problem is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain,when each choice's properties are only partially known at the time of allocation,and may become better understood as time passes or by allocating resources to the choice. This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. The name comes from imagining a gambler at a row of slot machines,who has to decide which machines to play,how many times to play each machine and in which order to play them,and whether to continue with the current machine or try a different machine. The multi-armed bandit problem also falls into the broad category of stochastic scheduling.
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems,the random variables appear in the formulation of the optimization problem itself,which involves random objective functions or random constraints. Stochastic optimization methods also include methods with random iterates. Some stochastic optimization methods use random iterates to solve stochastic problems,combining both meanings of stochastic optimization. Stochastic optimization methods generalize deterministic methods for deterministic problems.
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In computer science,online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step,as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset,requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data,or when the data itself is generated as a function of time,e.g.,stock price prediction. Online learning algorithms may be prone to catastrophic interference,a problem that can be addressed by incremental learning approaches.
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Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer,i.e. quantum-enhanced machine learning. While machine learning algorithms are used to compute immense quantities of data,quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. This includes hybrid methods that involve both classical and quantum processing,where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore,quantum algorithms can be used to analyze quantum states instead of classical data. Beyond quantum computing,the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments,such as learning the phase transitions of a quantum system or creating new quantum experiments. Quantum machine learning also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems,in particular neural networks. For example,some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa. Furthermore,researchers investigate more abstract notions of learning theory with respect to quantum information,sometimes referred to as "quantum learning theory".
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The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959,Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs,rather than following strictly static program instructions.
In machine learning,hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast,the values of other parameters are learned.
In machine learning and statistics,the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Since it influences to what extent newly acquired information overrides old information,it metaphorically represents the speed at which a machine learning model "learns". In the adaptive control literature,the learning rate is commonly referred to as gain.
James Milton Renegar Jr. is an American mathematician,specializing in optimization algorithms for linear programming and nonlinear programming.
References
- 1 2 3 "Elad Hazan".
- ↑ NJ.com, Rob Jennings | NJ Advance Media for (2018-12-19). "Self-driving cars, speech recognition. See Google's new AI lab at Princeton". nj. Retrieved 2021-07-31.
- ↑ "Elad Hazan - Google Scholar".
- ↑ "Teaching Machines to Learn by Themselves".
- ↑ "Google AI lab formally opens in downtown Princeton, bolstering innovation and invention".
- ↑ "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization" (PDF).
- ↑ Allen-Zhu, Zeyuan; Hazan, Elad; Hu, Wei; Li, Yuanzhi (2017). "Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls". arXiv: 1708.02105 [cs.LG].
- ↑ Hazan, Elad (2019). "Introduction to Online Convex Optimization". arXiv: 1909.05207 [cs.LG].
- ↑ "Pat Goldberg Memorial Best Paper Awards". 25 July 2016.
- ↑ "Google Research Awards – December 2011" (PDF).
- ↑ "Google Faculty Research Awards ¬ February 2015" (PDF).
- ↑ "ERC Starting Grants 2013 Results" (PDF).
- ↑ "Nokia announces its 2016 Bell Labs Prize winners".
- ↑ "Amazon Research Award - Recipients".
